用低开销纠错码让量子卷积神经网络在真实设备上稳定训练
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
- 采用双变量自行车码实现4阶纠错，降低量子噪声影响
- 4比特未纠错网络无法收敛，而纠错后学习率显著改善
- 适合追求实用化量子机器学习的科研与工程人员
量子卷积神经网络（QCNN）结合量子计算与经典卷积神经网络，在分类任务中具有加速潜力。然而，当前量子设备的噪声水平过高，难以实际运行QCNN。尽管表面码能实现低于阈值的错误率，但其量子比特开销过大。近期提出的双变量自行车（BB）码具备高容错阈值、恒定编码率和线性码距等优势。通过引入真实硬件噪声源的仿真，我们发现4比特未纠错的QCNN无法收敛，且学习率劣于纯数值模拟。为此，本文提出一种距离为4的BB量子纠错（QEC）技术用于QCNN。结果表明，该低开销纠错方案使QCNN向实际应用迈出关键一步。
原文摘要 · Abstract (English)
Quantum convolutional neural networks (QCNNs) combine the power of quantum computing and classical CNN for computational speedup in classification tasks. However, noise levels on state-of-the-art quantum devices remain too high for practical QCNN execution. In addition, despite the reliable surface code providing a method for error rates below a threshold value, they have a prohibitively large qubit cost. Recently introduced bivariate bicycle (BB) codes are of particular interest for their high error threshold, constant encoding rate, and linear code distance. Through simulation with realistic hardware noise sources, we demonstrate that a 4-qubit unprotected QCNN fails to converge and exhibits a worse learning rate compared to numerical simulations. Addressing both limitations, we propose a distance-4 BB quantum error-correction (QEC) technique for QCNNs. In doing so, we validate that our low-overhead QEC technique for QCNNS represents a step toward practical QCNNs.
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